The invention relates to electronic devices, and, more particularly, to detection in spread spectrum communications and related circuitry and methods.
Spread spectrum wireless communications utilize a radio frequency bandwidth greater than the minimum bandwidth required for the transmitted data rate, but many users may simultaneously occupy that bandwidth. For code division multiple access (CDMA) each of the many simultaneous users has one or more pseudo-random CDMA codes to “despread” by correlating the spread spectrum signals sent to that user and thereby recover the corresponding information data. Each pseudo-random CDMA code may be an orthogonal (Walsh) code, a pseudo-noise (PN) code, a Gold code, or combinations (modulo-2 additions) of such codes. After despreading the received signal at the correct time instant, the user recovers the corresponding information while the remaining interfering signals and noise appear noise-like. For example, the interim standard IS-95 for such CDMA communications employs channels of 1.25 MHz bandwidth and a code pulse (chip) interval TC of 0.8136 microsecond with 64 chips per transmitted bit. The wideband CDMA (WCDMA) proposal employs 3.84 MHz bandwidth and the CDMA code length applied to each information symbol may vary from 4 chips to 256 chips. The CDMA codes for each user are typically produced as the modulo-2 addition of Walsh codes with a pseudo-random code (two pseudo-random codes for QPSK modulation) to improve the noise-like nature of the resulting signal. A cellular system could employ IS-95 or WCDMA for the air interface between a base station and a mobile station.
Signals transmitted by multiple users simultaneously occupy the same frequency band in CDMA. A receiver discriminates among the multiple signals by exploiting the properties of the spreading and scrambling codes that are applied to the signal of each user. The receiver attempts to match in time with the codes of the desired signal a replica of those spreading and scrambling codes. Only then the demodulation result is meaningful; otherwise it appears noise-like. Thus, if the arriving signals have different codes or different code offsets, they can be discriminated at the receiver.
In the downlink of a cellular communication system (the communication from a base station to mobile terminals) the wireless channel may introduce multipath propagation. Even if the signals transmitted by the base station are spread using orthogonal codes (Walsh codes), the multipath propagation will destroy the orthogonality and produce multiple-access interference (MAI). In the uplink (the communication from a mobile terminal to a base station) the signals are asynchronously transmitted. Orthogonality in this case cannot be achieved and each signal will experience MAI.
Interference cancellation attempts to suppress the MAI by estimating and subtracting the contribution from each interfering user of interest from the received signal. This can be accomplished both before and after despreading. The challenge in the implementation of an interference cancellation method comes from the fact that it may have to be performed for many signals (and their multipath components). If the necessary parameters to implement interference cancellation have to be evaluated for every data symbol, the computation and storage requirements can place a significant burden on the overall receiver design. If instead, the previous parameters need to be updated at a slower rate than the symbol rate, the implementation is considerably simplified.
The prevailing considerations governing the implementation of interference cancellation are complexity and performance with the former being the dominant one. One implementation approach is to perform interference cancellation before despreading. Then, interference cancellation methods need to regenerate the signal of each interferer, based on a decision for each interfering signal's information symbol, phase, and received power, and subtract it from the received signal. Another implementation approach that avoids signal regeneration is to compute the code cross-correlations and perform interference cancellation on the decision statistic of the desired signal after despreading.
To cancel interference before despreading, the interfering signals of interest need to be regenerated at the receiver and then subtracted, according to some of a variety of possible interference cancellation approaches, from the received signal. The information needed to accurately describe and hence regenerate the interfering signals consists, for each such signal, of the signal power, the signal phase, the information data symbol, the beginning of the symbol period relative to the beginning of the symbol period of the desired signal at the receiver, and the spreading and scrambling codes.
Alternatively, the interference can be cancelled after despreading without having to re-spread and regenerate the signals. The interference effect after despreading is proportional to the code cross-correlations of the interfering and desired signals. If the codes were orthogonal, the effect of the interfering signals would be zero. For non-zero cross-correlations, the interfering signals have a non-zero effect on the decision statistics of the desired signal. The contribution of each interferer on the decision statistic of the desired signal can be removed if in addition to the previous information, the code cross-correlations are computed. Then, instead of regenerating each interfering signal and subtracting it from the received signal before despreading, the code cross-correlations are simply multiplied by each interfering signal's complex amplitude (power and phase) and information symbol. The result is subsequently subtracted from the output of the despreader for the desired for the desired signal.
For signals having the same data rate, the desired signal is affected by two consecutive symbols from each interferer. If the interfering signal is re-spread and subtracted from the received signal prior to despreading, the regenerated signal needs to be buffered and subtracted at the correct time instant. If the code cross-correlations are computed instead and interference cancellation is applied after despreading, two different cross-correlation values need to be computed for each of the two interfering symbols.
For signals having different data rates, the buffering requirements are determined by the lowest rate signal(s). The lowest rate signal is assigned a spreading code with the largest number of chips per symbol period. To perform interference cancellation by re-spreading, the number of chip samples required for buffering equals to the number of chips of the largest length spreading code. This also applies to higher data rate signals having a smaller number of chips per information symbol. The above statement assumes that the number of chips per symbol for any lower data rate signals(s) is an integer multiple of the number of chips per symbol for any of the higher data rate signal(s). Otherwise, the number of chip samples need to be buffered is equal to the least common multiple of all data rates.
If interference cancellation is performed after despreading, the number of code cross-correlations that has to be computed should also reflect the existence, if any, of different data rates. If N is the integer ceiling of the ratio between the lengths of the spreading codes assigned to a lower and a higher rate signal, the number of code cross-correlations that need to be evaluated to perform interference cancellation is equal to N for synchronous signal reception without orthogonal codes and equal to N+1 for asynchronous signal reception.
Hence the problem of cross-correlation computations for codes and correlations with received signals arises for interference cancellation methods and efficient plus flexible implementations are lacking.